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ویرایش: R2020a
نویسندگان: The MathWorks. Inc.
سری:
ناشر: The MathWorks, Inc.
سال نشر: 2020
تعداد صفحات: 2192
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 60 مگابایت
در صورت تبدیل فایل کتاب MATLAB Deep Learning Toolbox™ User's Guide به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب راهنمای کاربر MATLAB Deep Learning Toolbox™ نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Deep Networks Deep Learning in MATLAB What Is Deep Learning? Try Deep Learning in 10 Lines of MATLAB Code Start Deep Learning Faster Using Transfer Learning Train Classifiers Using Features Extracted from Pretrained Networks Deep Learning with Big Data on CPUs, GPUs, in Parallel, and on the Cloud Deep Learning with Big Data on GPUs and in Parallel Training with Multiple GPUs Deep Learning in the Cloud Fetch and Preprocess Data in Background Pretrained Deep Neural Networks Compare Pretrained Networks Load Pretrained Networks Feature Extraction Transfer Learning Import and Export Networks Learn About Convolutional Neural Networks Multiple-Input and Multiple-Output Networks Multiple-Input Networks Multiple-Output Networks List of Deep Learning Layers Deep Learning Layers Specify Layers of Convolutional Neural Network Image Input Layer Convolutional Layer Batch Normalization Layer ReLU Layer Cross Channel Normalization (Local Response Normalization) Layer Max and Average Pooling Layers Dropout Layer Fully Connected Layer Output Layers Set Up Parameters and Train Convolutional Neural Network Specify Solver and Maximum Number of Epochs Specify and Modify Learning Rate Specify Validation Data Select Hardware Resource Save Checkpoint Networks and Resume Training Set Up Parameters in Convolutional and Fully Connected Layers Train Your Network Deep Learning Tips and Tricks Choose Network Architecture Choose Training Options Improve Training Accuracy Fix Errors in Training Prepare and Preprocess Data Use Available Hardware Fix Errors With Loading from MAT-Files Long Short-Term Memory Networks LSTM Network Architecture Layers Classification, Prediction, and Forecasting Sequence Padding, Truncation, and Splitting Normalize Sequence Data Out-of-Memory Data Visualization LSTM Layer Architecture Deep Network Designer Transfer Learning with Deep Network Designer Build Networks with Deep Network Designer Open App and Import Networks Create and Edit a Network Check Network Train Network Using Deep Network Designer Export Network Create Simple Sequence Classification Network Using Deep Network Designer Generate MATLAB Code from Deep Network Designer Generate MATLAB Code to Recreate Network Layers Generate MATLAB Code to Train Network Deep Learning with Images Classify Webcam Images Using Deep Learning Train Deep Learning Network to Classify New Images Train Residual Network for Image Classification Classify Image Using GoogLeNet Extract Image Features Using Pretrained Network Transfer Learning Using AlexNet Create Simple Deep Learning Network for Classification Train Convolutional Neural Network for Regression Train Network with Multiple Outputs Convert Classification Network into Regression Network Train Generative Adversarial Network (GAN) Train Conditional Generative Adversarial Network (CGAN) Train a Siamese Network to Compare Images Train a Siamese Network for Dimensionality Reduction Train Variational Autoencoder (VAE) to Generate Images Deep Learning with Time Series, Sequences, and Text Sequence Classification Using Deep Learning Time Series Forecasting Using Deep Learning Speech Command Recognition Using Deep Learning Sequence-to-Sequence Classification Using Deep Learning Sequence-to-Sequence Regression Using Deep Learning Classify Videos Using Deep Learning Sequence-to-Sequence Classification Using 1-D Convolutions Classify Text Data Using Deep Learning Classify Text Data Using Convolutional Neural Network Multilabel Text Classification Using Deep Learning Sequence-to-Sequence Translation Using Attention Generate Text Using Deep Learning Pride and Prejudice and MATLAB Word-By-Word Text Generation Using Deep Learning Image Captioning Using Attention Deep Learning Tuning and Visualization Deep Dream Images Using GoogLeNet Grad-CAM Reveals the Why Behind Deep Learning Decisions Understand Network Predictions Using Occlusion Investigate Classification Decisions Using Gradient Attribution Techniques Resume Training from Checkpoint Network Deep Learning Using Bayesian Optimization Run Multiple Deep Learning Experiments in Parallel Monitor Deep Learning Training Progress Customize Output During Deep Learning Network Training Investigate Network Predictions Using Class Activation Mapping View Network Behavior Using tsne Visualize Activations of a Convolutional Neural Network Visualize Activations of LSTM Network Visualize Features of a Convolutional Neural Network Visualize Image Classifications Using Maximal and Minimal Activating Images Monitor GAN Training Progress and Identify Common Failure Modes Convergence Failure Mode Collapse Manage Deep Learning Experiments Create a Deep Learning Experiment for Classification Create a Deep Learning Experiment for Regression Evaluate Deep Learning Experiments by Using Metric Functions Try Multiple Pretrained Networks for Transfer Learning Experiment with Weight Initializers for Transfer Learning Deep Learning in Parallel and the Cloud Scale Up Deep Learning in Parallel and in the Cloud Deep Learning on Multiple GPUs Deep Learning in the Cloud Advanced Support for Fast Multi-Node GPU Communication Deep Learning with MATLAB on Multiple GPUs Select Particular GPUs to Use for Training Train Network in the Cloud Using Automatic Parallel Support Train Network in the Cloud Using Automatic Parallel Support Use parfeval to Train Multiple Deep Learning Networks Send Deep Learning Batch Job to Cluster Train Network Using Automatic Multi-GPU Support Use parfor to Train Multiple Deep Learning Networks Upload Deep Learning Data to the Cloud Train Network in Parallel with Custom Training Loop Computer Vision Examples Point Cloud Classification Using PointNet Deep Learning Import Pretrained ONNX YOLO v2 Object Detector Export YOLO v2 Object Detector to ONNX Object Detection Using SSD Deep Learning Object Detection Using YOLO v3 Deep Learning Object Detection Using YOLO v2 Deep Learning Semantic Segmentation Using Deep Learning Semantic Segmentation Using Dilated Convolutions Semantic Segmentation of Multispectral Images Using Deep Learning 3-D Brain Tumor Segmentation Using Deep Learning Define Custom Pixel Classification Layer with Tversky Loss Train Object Detector Using R-CNN Deep Learning Object Detection Using Faster R-CNN Deep Learning Image Processing Examples Remove Noise from Color Image Using Pretrained Neural Network Single Image Super-Resolution Using Deep Learning JPEG Image Deblocking Using Deep Learning Image Processing Operator Approximation Using Deep Learning Deep Learning Classification of Large Multiresolution Images Generate Image from Segmentation Map Using Deep Learning Neural Style Transfer Using Deep Learning Automated Driving Examples Train a Deep Learning Vehicle Detector Create Occupancy Grid Using Monocular Camera and Semantic Segmentation Signal Processing Examples Radar Waveform Classification Using Deep Learning Pedestrian and Bicyclist Classification Using Deep Learning Label QRS Complexes and R Peaks of ECG Signals Using Deep Network Waveform Segmentation Using Deep Learning Modulation Classification with Deep Learning Classify ECG Signals Using Long Short-Term Memory Networks Classify Time Series Using Wavelet Analysis and Deep Learning Audio Examples Train Generative Adversarial Network (GAN) for Sound Synthesis Sequential Feature Selection for Audio Features Acoustic Scene Recognition Using Late Fusion Keyword Spotting in Noise Using MFCC and LSTM Networks Speech Emotion Recognition Spoken Digit Recognition with Wavelet Scattering and Deep Learning Cocktail Party Source Separation Using Deep Learning Networks Voice Activity Detection in Noise Using Deep Learning Denoise Speech Using Deep Learning Networks Classify Gender Using LSTM Networks Reinforcement Learning Examples Create Simulink Environment and Train Agent Train DDPG Agent to Swing Up and Balance Pendulum with Image Observation Create Agent Using Deep Network Designer and Train Using Image Observations Train DDPG Agent to Control Flying Robot Train Biped Robot to Walk Using Reinforcement Learning Agents Train DDPG Agent for Adaptive Cruise Control Train DQN Agent for Lane Keeping Assist Using Parallel Computing Train DDPG Agent for Path Following Control Predictive Maintenance Examples Chemical Process Fault Detection Using Deep Learning Automatic Differentiation Define Custom Deep Learning Layers Layer Templates Intermediate Layer Architecture Check Validity of Layer Include Layer in Network Output Layer Architecture Define Custom Deep Learning Layer with Learnable Parameters Layer with Learnable Parameters Template Name the Layer Declare Properties and Learnable Parameters Create Constructor Function Create Forward Functions Completed Layer GPU Compatibility Check Validity of Layer Using checkLayer Include Custom Layer in Network Define Custom Deep Learning Layer with Multiple Inputs Layer with Learnable Parameters Template Name the Layer Declare Properties and Learnable Parameters Create Constructor Function Create Forward Functions Completed Layer GPU Compatibility Check Validity of Layer with Multiple Inputs Use Custom Weighted Addition Layer in Network Define Custom Classification Output Layer Classification Output Layer Template Name the Layer Declare Layer Properties Create Constructor Function Create Forward Loss Function Completed Layer GPU Compatibility Check Output Layer Validity Include Custom Classification Output Layer in Network Define Custom Weighted Classification Layer Classification Output Layer Template Name the Layer Declare Layer Properties Create Constructor Function Create Forward Loss Function Completed Layer GPU Compatibility Check Output Layer Validity Define Custom Regression Output Layer Regression Output Layer Template Name the Layer Declare Layer Properties Create Constructor Function Create Forward Loss Function Completed Layer GPU Compatibility Check Output Layer Validity Include Custom Regression Output Layer in Network Specify Custom Layer Backward Function Create Custom Layer Create Backward Function Complete Layer GPU Compatibility Specify Custom Output Layer Backward Loss Function Create Custom Layer Create Backward Loss Function Complete Layer GPU Compatibility Check Custom Layer Validity Check Layer Validity List of Tests Generated Data Diagnostics Specify Custom Weight Initialization Function Compare Layer Weight Initializers Assemble Network from Pretrained Keras Layers Assemble Multiple-Output Network for Prediction Automatic Differentiation Background What Is Automatic Differentiation? Forward Mode Reverse Mode Use Automatic Differentiation In Deep Learning Toolbox Custom Training and Calculations Using Automatic Differentiation Use dlgradient and dlfeval Together for Automatic Differentiation Derivative Trace Characteristics of Automatic Derivatives Define Custom Training Loops, Loss Functions, and Networks Define Custom Training Loops Define Custom Networks Specify Training Options in Custom Training Loop Solver Options Learn Rate Plots Verbose Output Mini-Batch Size Number of Epochs Validation L2 Regularization Gradient Clipping Single CPU or GPU Training Checkpoints Train Network Using Custom Training Loop Update Batch Normalization Statistics in Custom Training Loop Make Predictions Using dlnetwork Object Train Network Using Model Function Update Batch Normalization Statistics Using Model Function Make Predictions Using Model Function Train Network Using Cyclical Learn Rate for Snapshot Ensembling List of Functions with dlarray Support Deep Learning Toolbox Functions with dlarray Support MATLAB Functions with dlarray Support Notable dlarray Behaviors Deep Learning Data Preprocessing Datastores for Deep Learning Select Datastore Input Datastore for Training, Validation, and Inference Specify Read Size and Mini-Batch Size Transform and Combine Datastores Use Datastore for Parallel Training and Background Dispatching Preprocess Images for Deep Learning Resize Images Using Rescaling and Cropping Augment Images for Training with Random Geometric Transformations Perform Additional Image Processing Operations Using Built-In Datastores Apply Custom Image Processing Pipelines Using Combine and Transform Preprocess Volumes for Deep Learning Read Volumetric Data Associate Image and Label Data Preprocess Volumetric Data Preprocess Data for Domain-Specific Deep Learning Applications Image Processing Applications Object Detection Semantic Segmentation Signal Processing Applications Audio Processing Applications Text Analytics Develop Custom Mini-Batch Datastore Overview Implement MiniBatchable Datastore Add Support for Shuffling Validate Custom Mini-Batch Datastore Augment Images for Deep Learning Workflows Using Image Processing Toolbox Augment Pixel Labels for Semantic Segmentation Augment Bounding Boxes for Object Detection Prepare Datastore for Image-to-Image Regression Train Network Using Out-of-Memory Sequence Data Train Network Using Custom Mini-Batch Datastore for Sequence Data Classify Out-of-Memory Text Data Using Deep Learning Classify Out-of-Memory Text Data Using Custom Mini-Batch Datastore Data Sets for Deep Learning Image Data Sets Time Series and Signal Data Sets Video Data Sets Text Data Sets Audio Data Sets Deep Learning Code Generation Code Generation for Deep Learning Networks Code Generation for Semantic Segmentation Network Lane Detection Optimized with GPU Coder Code Generation for a Sequence-to-Sequence LSTM Network Deep Learning Prediction on ARM Mali GPU Code Generation for Object Detection by Using YOLO v2 Integrating Deep Learning with GPU Coder into Simulink Deep Learning Prediction by Using NVIDIA TensorRT Deep Learning Prediction by Using Different Batch Sizes Traffic Sign Detection and Recognition Logo Recognition Network Pedestrian Detection Code Generation for Denoising Deep Neural Network Train and Deploy Fully Convolutional Networks for Semantic Segmentation Code Generation for Semantic Segmentation Network by Using U-net Code Generation for Deep Learning on ARM Targets Code Generation for Deep Learning on Raspberry Pi Deep Learning Prediction with ARM Compute Using cnncodegen Deep Learning Prediction with Intel MKL-DNN Generate C++ Code for Object Detection Using YOLO v2 and Intel MKL-DNN Code Generation and Deployment of MobileNet-v2 Network to Raspberry Pi Neural Network Design Book Neural Network Objects, Data, and Training Styles Workflow for Neural Network Design Four Levels of Neural Network Design Neuron Model Simple Neuron Transfer Functions Neuron with Vector Input Neural Network Architectures One Layer of Neurons Multiple Layers of Neurons Input and Output Processing Functions Create Neural Network Object Configure Shallow Neural Network Inputs and Outputs Understanding Shallow Network Data Structures Simulation with Concurrent Inputs in a Static Network Simulation with Sequential Inputs in a Dynamic Network Simulation with Concurrent Inputs in a Dynamic Network Neural Network Training Concepts Incremental Training with adapt Batch Training Training Feedback Multilayer Shallow Neural Networks and Backpropagation Training Multilayer Shallow Neural Networks and Backpropagation Training Multilayer Shallow Neural Network Architecture Neuron Model (logsig, tansig, purelin) Feedforward Neural Network Prepare Data for Multilayer Shallow Neural Networks Choose Neural Network Input-Output Processing Functions Representing Unknown or Don\'t-Care Targets Divide Data for Optimal Neural Network Training Create, Configure, and Initialize Multilayer Shallow Neural Networks Other Related Architectures Initializing Weights (init) Train and Apply Multilayer Shallow Neural Networks Training Algorithms Training Example Use the Network Analyze Shallow Neural Network Performance After Training Improving Results Limitations and Cautions Dynamic Neural Networks Introduction to Dynamic Neural Networks How Dynamic Neural Networks Work Feedforward and Recurrent Neural Networks Applications of Dynamic Networks Dynamic Network Structures Dynamic Network Training Design Time Series Time-Delay Neural Networks Prepare Input and Layer Delay States Design Time Series Distributed Delay Neural Networks Design Time Series NARX Feedback Neural Networks Multiple External Variables Design Layer-Recurrent Neural Networks Create Reference Model Controller with MATLAB Script Multiple Sequences with Dynamic Neural Networks Neural Network Time-Series Utilities Train Neural Networks with Error Weights Normalize Errors of Multiple Outputs Multistep Neural Network Prediction Set Up in Open-Loop Mode Multistep Closed-Loop Prediction From Initial Conditions Multistep Closed-Loop Prediction Following Known Sequence Following Closed-Loop Simulation with Open-Loop Simulation Control Systems Introduction to Neural Network Control Systems Design Neural Network Predictive Controller in Simulink System Identification Predictive Control Use the Neural Network Predictive Controller Block Design NARMA-L2 Neural Controller in Simulink Identification of the NARMA-L2 Model NARMA-L2 Controller Use the NARMA-L2 Controller Block Design Model-Reference Neural Controller in Simulink Use the Model Reference Controller Block Import-Export Neural Network Simulink Control Systems Import and Export Networks Import and Export Training Data Radial Basis Neural Networks Introduction to Radial Basis Neural Networks Important Radial Basis Functions Radial Basis Neural Networks Neuron Model Network Architecture Exact Design (newrbe) More Efficient Design (newrb) Examples Probabilistic Neural Networks Network Architecture Design (newpnn) Generalized Regression Neural Networks Network Architecture Design (newgrnn) Self-Organizing and Learning Vector Quantization Networks Introduction to Self-Organizing and LVQ Important Self-Organizing and LVQ Functions Cluster with a Competitive Neural Network Architecture Create a Competitive Neural Network Kohonen Learning Rule (learnk) Bias Learning Rule (learncon) Training Graphical Example Cluster with Self-Organizing Map Neural Network Topologies (gridtop, hextop, randtop) Distance Functions (dist, linkdist, mandist, boxdist) Architecture Create a Self-Organizing Map Neural Network (selforgmap) Training (learnsomb) Examples Learning Vector Quantization (LVQ) Neural Networks Architecture Creating an LVQ Network LVQ1 Learning Rule (learnlv1) Training Supplemental LVQ2.1 Learning Rule (learnlv2) Adaptive Filters and Adaptive Training Adaptive Neural Network Filters Adaptive Functions Linear Neuron Model Adaptive Linear Network Architecture Least Mean Square Error LMS Algorithm (learnwh) Adaptive Filtering (adapt) Advanced Topics Neural Networks with Parallel and GPU Computing Deep Learning Modes of Parallelism Distributed Computing Single GPU Computing Distributed GPU Computing Parallel Time Series Parallel Availability, Fallbacks, and Feedback Optimize Neural Network Training Speed and Memory Memory Reduction Fast Elliot Sigmoid Choose a Multilayer Neural Network Training Function SIN Data Set PARITY Data Set ENGINE Data Set CANCER Data Set CHOLESTEROL Data Set DIABETES Data Set Summary Improve Shallow Neural Network Generalization and Avoid Overfitting Retraining Neural Networks Multiple Neural Networks Early Stopping Index Data Division (divideind) Random Data Division (dividerand) Block Data Division (divideblock) Interleaved Data Division (divideint) Regularization Summary and Discussion of Early Stopping and Regularization Posttraining Analysis (regression) Edit Shallow Neural Network Properties Custom Network Network Definition Network Behavior Custom Neural Network Helper Functions Automatically Save Checkpoints During Neural Network Training Deploy Shallow Neural Network Functions Deployment Functions and Tools for Trained Networks Generate Neural Network Functions for Application Deployment Generate Simulink Diagrams Deploy Training of Shallow Neural Networks Historical Neural Networks Historical Neural Networks Overview Perceptron Neural Networks Neuron Model Perceptron Architecture Create a Perceptron Perceptron Learning Rule (learnp) Training (train) Limitations and Cautions Linear Neural Networks Neuron Model Network Architecture Least Mean Square Error Linear System Design (newlind) Linear Networks with Delays LMS Algorithm (learnwh) Linear Classification (train) Limitations and Cautions Neural Network Object Reference Neural Network Object Properties General Architecture Subobject Structures Functions Weight and Bias Values Neural Network Subobject Properties Inputs Layers Outputs Biases Input Weights Layer Weights Function Approximation, Clustering, and Control Examples Body Fat Estimation Crab Classification Wine Classification Cancer Detection Character Recognition Train Stacked Autoencoders for Image Classification Iris Clustering Gene Expression Analysis Maglev Modeling Competitive Learning One-Dimensional Self-organizing Map Two-Dimensional Self-organizing Map Radial Basis Approximation Radial Basis Underlapping Neurons Radial Basis Overlapping Neurons GRNN Function Approximation PNN Classification Learning Vector Quantization Linear Prediction Design Adaptive Linear Prediction Classification with a 2-Input Perceptron Outlier Input Vectors Normalized Perceptron Rule Linearly Non-separable Vectors Pattern Association Showing Error Surface Training a Linear Neuron Linear Fit of Nonlinear Problem Underdetermined Problem Linearly Dependent Problem Too Large a Learning Rate Adaptive Noise Cancellation Shallow Neural Networks Bibliography Shallow Neural Networks Bibliography Mathematical Notation Mathematics and Code Equivalents Mathematics Notation to MATLAB Notation Figure Notation Neural Network Blocks for the Simulink Environment Neural Network Simulink Block Library Transfer Function Blocks Net Input Blocks Weight Blocks Processing Blocks Deploy Shallow Neural Network Simulink Diagrams Example Suggested Exercises Generate Functions and Objects Code Notes Deep Learning Toolbox Data Conventions Dimensions Variables